(295a) Modeling Ternary Alloy Segregation with Density Functional Theory and Machine Learning

It is well known that the surface composition of an alloy has a significant effect on its reactivity. However, due to segregation the surface composition is rarely the same as the bulk composition, and in reactive environments adsorbates can further influence the surface composition. It is difficult to measure surface composition under reaction conditions, and it is appealing to try to simulate it. In this talk, we will show how we approach segregation in a ternary Cu-Pd-Au alloy system using density functional theory, machine learning and Monte Carlo simulations. We compare the simulated results to experimental measurements across the composition space. We found good agreement in some composition spaces, but poor agreement in others. We will discuss many reasons for the discrepancies observed, and highlight some limitations in the simulation of these systems.